Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Analysis to Recommendation and Decision Framing
Ability to move from analysis to a concise, justified recommendation and a pragmatic plan for decision and implementation. Candidates should lead with a clear recommendation or conditional decision, support it with evidence and trade offs, quantify expected business impact, estimate effort and time horizon, and state assumptions and limitations. The skill set includes proposing prioritized action plans and alternative options, anticipating objections, defining monitoring and rollback strategies, translating technical remediation or risk into business terms and measurable success metrics, and tailoring recommendations to stakeholder needs and constraints.
Engineering and Business Outcomes
How engineering work and technical decisions translate into measurable business outcomes and how to demonstrate that linkage. Topics include mapping architecture choices, reliability, performance improvements and developer productivity initiatives to business metrics such as revenue, customer engagement, time to market, cost reduction and customer satisfaction. Candidates should be able to identify engineering metrics to track including latency, availability, error and incident rates, cycle time and deployment frequency, explain instrumentation strategies to capture signals, design measurement plans and experiments to establish causal impact, and attribute observed changes to specific engineering efforts. This topic also covers communicating technical tradeoffs and impact to nontechnical stakeholders, choosing appropriate granularity for measurement, and describing concrete initiatives with their measurement approach and quantified business impact.
Data Interpretation & Dashboard Literacy
Practice interpreting data visualizations, trend lines, and metric dashboards. Develop ability to identify what's noteworthy (seasonality, anomalies, correlations) vs. normal variation. Think about causation vs. correlation. Practice explaining what a metric trend means in business terms and what actions it might suggest.
Metrics Analysis and Data Driven Problem Solving
Skills for using quantitative metrics to diagnose and solve business, product, or operational problems across functions. Candidates should be able to identify the key performance indicators relevant to their domain (for example: conversion rate, retention, revenue per user, pipeline velocity, response time, or customer satisfaction), detect anomalies and trends in metrics, formulate and prioritize hypotheses about root causes, design experiments and controlled tests (such as A/B tests) to validate hypotheses, perform cohort and time series analysis, evaluate statistical significance versus practical business impact, and implement and monitor data backed solutions. This also includes instrumentation and data collection best practices, dashboarding and visualization to surface insights, trade off analysis when balancing multiple competing metrics, and communicating findings and recommended changes to cross functional stakeholders.
Interest in Data and Analytics
Evaluates a candidate's genuine curiosity about working with data and their practical comfort with quantitative information, spreadsheets, dashboards, reporting, and analytics tools. Strong responses describe specific hands on experience with data analysis, measurement, reporting, or analytics projects, including concrete examples of metrics tracked, analyses performed, dashboards or reports built, and outcomes or decisions influenced by those insights. Candidates should be able to articulate learning activities and motivations such as courses, personal or open source projects, reading, or tool exploration, and to candidly identify development areas such as structured query language, statistical methods, experiment design, or visualization techniques. The topic also assesses the candidate's ability to explain why data matters for the role and how they use evidence to inform product, process, or business decisions.
Customer and Marketing Performance Analytics
Covers the end to end use of quantitative analysis to track, interpret, and act on business performance across accounts and campaigns. Candidates should be fluent in account level metrics such as customer retention rate, net revenue retention, annual recurring revenue, net promoter score, customer health scores, and customer lifetime value, as well as marketing and acquisition metrics such as click through rate, conversion rate, customer acquisition cost, return on advertising spend, and attribution model outcomes. Expect discussion of data sources and instrumentation, cohort and funnel analysis, segmentation, anomaly detection, attribution approaches, and calculating return on investment for initiatives. Candidates should be able to describe how they used analytics tools and queries, dashboards, and experiments or A B tests to identify at risk accounts or underperforming campaigns, prioritize actions, optimize strategies, and measure the impact of initiatives. Strong answers explain concrete metrics chosen, analysis methods, tools used, how results informed decisions, and how success was measured over time.
Program Evaluation and Measurement
Assessing whether a program, initiative, or intervention achieves its intended objectives and delivers measurable value, across domains such as training and development, product or feature rollouts, operational process changes, and organizational or culture initiatives. This includes defining success criteria and baseline metrics before implementation, selecting quantitative and qualitative measures during and after delivery, and evaluating impact across multiple levels: immediate reaction, learning or adoption, behavior or usage change, and downstream business results (the logic behind frameworks like the Kirkpatrick model, applied broadly to any program with a change-in-behavior goal, not only training). Candidates should be able to design evaluation plans that include completion and engagement metrics, knowledge or skill assessments, behavior or application measures, retention or usage indicators, and business outcomes. The topic covers leading and lagging indicators, approaches to isolating program impact from confounding factors, simple experimental or quasi-experimental designs when feasible, pragmatic trade offs between ideal and practical measurement, data collection methods and tools, calculating and communicating return on investment (both financial and non-financial), and tailoring reporting to different stakeholders. Examples might include measuring onboarding's effect on time to productivity, a new internal tool's effect on team throughput, a communications campaign's effect on feature adoption, or a process change's effect on error rates. For junior level roles, demonstrate familiarity with measurement choices and their limitations; for senior level roles, include designing robust evaluation frameworks and translating findings into business recommendations.
Data and Business Outcomes
This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.
Technical Foundations for Business Analysis
This topic evaluates the baseline technical knowledge that enables a business analyst to turn business questions into verifiable analysis and to collaborate effectively with engineers and data teams. Candidates should be familiar with structured query language fundamentals for extracting and aggregating data, common business intelligence and spreadsheet tooling, basic data modeling and entity relationship concepts, and systems thinking to trace data flows and diagnose issues. Strong responses illustrate how the candidate uses these skills to validate requirements, design testable acceptance criteria, and identify when to escalate complex technical problems.